<ul><li>The practice of utilizing task-specific fine-tuning has been implemented to improve the performance of large language models (LLM) in subsequent tasks.</li><li>Model merging strategies have emerged to reduce the memory footprint during inference by merging all LLMs into one model.</li><li>To mitigate parameter conflicts and improve storage efficiency, a novel strategy called Channel Merging is introduced.</li><li>Channel Merging clusters and merges channel parameters based on their similarity, reducing conflicts while preserving specialized knowledge.</li></ul>

Channel Merging: Preserving Specialization for Merged Experts

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